• Load microarray dataset into R
• Explore the dataset with basic visualizations
• Identify differentially expressed genes (DEGs)
• Generate annotation of the DEGs (Tentative)

DNA makes RNA and RNA makes protein

Cleft lip and cleft palate (CLP) are splits in the upper lip, the roof of the mouth (palate) or both. They result when facial structures that are developing in an unborn baby do not close completely. CLP is one of the most common birth defects with a frequency of 1/700 live births.

Cleft lip and palate

Children with cleft lip with or without cleft palate face a variety of challenges, depending on the type and severity of the cleft.

• Difficulty feeding. One of the most immediate concerns after birth is feeding.

• Ear infections and hearing loss. Babies with cleft palate are especially at risk of developing middle ear fluid and hearing loss.

• Dental problems. If the cleft extends through the upper gum, tooth development may be affected.

• Speech difficulties. Because the palate is used in forming sounds, the development of normal speech can be affected by a cleft palate. Speech may sound too nasal.

Reference: Mayo Foundation for Medical Education and Research

• DNA variation in Interferon Regulatory Factor 6 (IRF6) causes Van der Woude syndrome (VWS)

• VWS is the most common syndromic form of cleft lip and palate.

• However, the causing variant in IRF6 has been found in only 70% of VWS families!

• IRF6 is a transcription factor with a conserved helix-loop-helix DNA binding domain and a less well-conserved protein binding domain.

Reference: Hum Mol Genet. 2014 May 15; 23(10): 2711–2720

Given:

1. The pathogenic variant in IRF6 exists in only 70% of the VWS families

2. IRF6 is a transcription factor

How can we identify other genes that might be involved in the remaining 30% of the VWS families?

• Usually, genes that are regulated by a transcription factor belong to the same biological process or pathway.

• Therefore, by comparing the gene expression patterns between wild-type (functional) Irf6 and knockout (non-functional) Irf6, it could be possible to identify genes that are regulated (targeted) by Irf6.

• \(H_O : \mu_{WT} = \mu_{KO}\)

• \(H_A : \mu_{WT} \ne \mu_{KO}\)

• Where \(\mu\) is the mean of the gene expression values of a gene.

• One-sided or Two-sided testing?

• No need for candidate genes (or genes of interest)

• One experiment assesses the entire transcriptome

• One experiment generates many hypotheses

• Only small amount of RNA is required (~15–200 ng)

PMID: 17041601

• 3 IRF6 wild-type (+/+) and 3 knockout (-/-) mouse embryos.
• E17.5 embryos were removed from euthanized mothers.
• Skin was removed from embryos.
• Total RNA was isolated from the skin.
• Resultant RNA was hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays.

• The original dataset can be obtained from NCBI GEO with accession GSE5800

First, we are going to load the dataset from the .tsv file into R as a variable called data using the read.table function.
data is just an arbitrary varilable name to hold the result of read.table and it can be called/named almost anything.

# Load the data from a file into a varilable
data = read.table("https://raw.githubusercontent.com/ahmedmoustafa/AUCBIOT5206/master/microarray/datasets/irf6.tsv", header = TRUE, row.names = 1)

# Convert the data.frame (table) in a matrix (numeric)
data = as.matrix(data)

Note: the hash sign (#) indicates that what comes after is a comment. Comments are for documentation and readability of the R code and they are not evaluated (or executed).

dim(data) # Dimension of the dataset

## [1] 45101     6

head(data) # First few rows

number_of_genes = nrow(data) # number of genes = number of rows
number_of_genes

## [1] 45101

ids = row.names(data) # The ids of the genes are the names of the rows
head(ids)

## [1] "1415670_at"   "1415671_at"   "1415672_at"   "1415673_at"   "1415674_a_at"
## [6] "1415675_at"

Check the behavior of the data (e.g., normal?, skewed?)

hist(data, col = "gray", main="Histogram")

\(log_2\) transformation (why?)

data2 = log2(data)
hist(data2, col = "gray")

colors = c(rep("navy", 3), rep("orange", 3))
boxplot(data2, col = colors)

Hierarchical clustering of the samples (i.e., columns) based on the correlation coefficients of the expression values

hc = hclust(as.dist(1 - cor(data2)))
plot(hc)

To learn more about a function (e.g., hclust), you may type ?function (e.g., ?hclust) in the console to launch R documentation on that function:

ko = data2[, 1:3] # KO matrix
head(ko)

wt = data2[, 4:6] # WT matrix
head(wt)

# Compute the means of the KO samples
ko.means = rowMeans(ko)
head(ko.means)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at   1415675_at 
##     12.61692     13.40966     13.78313     11.47096     11.84693     11.72381

# Compute the means of the WT samples
wt.means = rowMeans(wt)
head(wt.means)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at   1415675_at 
##     12.87043     13.15269     13.70450     11.20664     11.66649     11.55578

plot(ko.means ~ wt.means) # The actual scatter plot
abline(0, 1, col = "red") # Only a diagonal line

pairs(data2) # All pairwise comparisons

To identify DEGs, we will identify:

• Biologically significantly differentially expressed
• Statistically significantly differentially expressed

Then, we will take the overlap (intersection) of the two sets

fold = ko.means - wt.means # Difference between means
head(fold)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at   1415675_at 
##  -0.25351267   0.25697097   0.07863227   0.26431191   0.18044345   0.16803065

• What do the positive and negative values of the fold-change indicate? Considering the WT condition is the reference (or control)

• +ve fold-change \(\rightarrow\) Up-regulation \(\uparrow\)

• -ve fold-change \(\rightarrow\) Down-regulation \(\downarrow\)

hist(fold, col = "gray") # Histogram of the fold

• To assess the statistical significance of the difference in the expression values for each gene between the two conditions (e.g., WT and KO), we are going to use t-test.

Let’s say there are two samples x and y from the two populations, X and Y, respectively, to determine whether the means of two populations are significantly different, we can use t.test.

?t.test

t.test(x, ...)

## Default S3 method:
t.test(x, y = NULL,
       alternative = c("two.sided", "less", "greater"),
       mu = 0, paired = FALSE, var.equal = FALSE,
       conf.level = 0.95, ...)

## S3 method for class 'formula'
t.test(formula, data, subset, na.action, ...)

require(graphics)

t.test(1:10, y = c(7:20))      # P = .00001855
t.test(1:10, y = c(7:20, 200)) # P = .1245    -- NOT significant anymore

## Classical example: Student's sleep data
plot(extra ~ group, data = sleep)
## Traditional interface
with(sleep, t.test(extra[group == 1], extra[group == 2]))
## Formula interface
t.test(extra ~ group, data = sleep)

x = c(4, 3, 10, 7, 9) ; y = c(7, 4, 3, 8, 10)
t.test(x, y)

## 
##  Welch Two Sample t-test
## 
## data:  x and y
## t = 0.1066, df = 7.9743, p-value = 0.9177
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.12888  4.52888
## sample estimates:
## mean of x mean of y 
##       6.6       6.4

t.test(x, y)$p.value

## [1] 0.917739

x = c(6, 8, 10, 7, 9) ; y = c(3, 2, 1, 4, 5)
t.test(x, y)

## 
##  Welch Two Sample t-test
## 
## data:  x and y
## t = 5, df = 8, p-value = 0.001053
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.693996 7.306004
## sample estimates:
## mean of x mean of y 
##         8         3

t.test(x, y)$p.value

## [1] 0.001052826

Let’s compute the p-value for all genes using a for-loop of t.test, one gene at a time:

pvalue = NULL # Empty list for the p-values

for(i in 1 : number_of_genes) { # for each gene from to the number of genes
  x = wt[i, ] # wt values of gene number i
  y = ko[i, ] # ko values of gene number i
  t = t.test(x, y) # t-test between the two conditions
  pvalue[i] = t$p.value # Store p-value number i into the list of p-values
}
head(pvalue)

## [1] 0.092706280 0.182663337 0.129779075 0.272899180 0.262377176 0.005947807

hist(-log10(pvalue), col = "gray") # Histogram of p-values (-log10)

plot(-log10(pvalue) ~ fold)

fold_cutoff = 2
pvalue_cutoff = 0.01

plot(-log10(pvalue) ~ fold)

abline(v = fold_cutoff, col = "blue", lwd = 3)
abline(v = -fold_cutoff, col = "red", lwd = 3)
abline(h = -log10(pvalue_cutoff), col = "green", lwd = 3)

filter_by_fold = abs(fold) >= fold_cutoff # Biological
sum(filter_by_fold) # Number of genes staisfy the condition

## [1] 1051

filter_by_pvalue = pvalue <= pvalue_cutoff # Statistical
sum(filter_by_pvalue)

## [1] 1564

filter_combined = filter_by_fold & filter_by_pvalue # Combined
sum(filter_combined)

## [1] 276

filtered = data2[filter_combined, ]
dim(filtered)

## [1] 276   6

head(filtered)

plot(-log10(pvalue) ~ fold)
points(-log10(pvalue[filter_combined]) ~ fold[filter_combined],
       col = "green")

On the volcano plot, highlight the up-regulated genes in red and the download-regulated genes in blue

• Up-regulated genes

# Screen for the up-regulated genes (+ve fold)
filter_up = filter_combined & fold > 0

head(filter_up)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at   1415675_at 
##        FALSE        FALSE        FALSE        FALSE        FALSE        FALSE

# Number of filtered genes
sum(filter_up)

## [1] 95

• Down-regulated genes

# Screen for the down-regulated genes (-ve fold)
filter_down = filter_combined & fold < 0

head(filter_down)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at   1415675_at 
##        FALSE        FALSE        FALSE        FALSE        FALSE        FALSE

# Number of filtered genes
sum(filter_down)

## [1] 181

plot(-log10(pvalue) ~ fold)
points(-log10(pvalue[filter_up]) ~ fold[filter_up], col = "red")
points(-log10(pvalue[filter_down]) ~ fold[filter_down], col = "blue")

heatmap(filtered)

• By default, heatmap clusters genes (rows) and samples (columns) based on the Euclidean distance.

• In the context of gene expression, we need to cluster genes and samples based on the correlation to explore patterns of co-regulation (co-expression) - Guilt by Association.

• To let heatmap cluster the genes and/or samples, the genes and samples will be clustered (grouped) by correlation coefficients (using cor) among the genes and samples.

# Clustering of the columns (samples)
col_dendrogram = as.dendrogram(hclust(as.dist(1-cor(filtered))))

# Clustering of the rows (genes)
row_dendrogram = as.dendrogram(hclust(as.dist(1-cor(t(filtered)))))

# Heatmap with the rows and columns clustered by correlation coefficients
heatmap(filtered, Rowv=row_dendrogram, Colv=col_dendrogram)

library(gplots) # Load the gplots library
heatmap(filtered, Rowv=row_dendrogram, Colv=col_dendrogram, col = rev(redgreen(1024)))

To obtain the functional annotation of the differentially expressed genes, we are going first to extract their probe ids:

filterd_ids = row.names(filtered) # ids of the filtered DE genes
length(filterd_ids)

## [1] 276

head(filterd_ids)

## [1] "1416200_at"   "1416236_a_at" "1417808_at"   "1417932_at"   "1418050_at"  
## [6] "1418100_at"

Then we will generate a comprehensive functional annotation via BioConductor packages annaffy and mouse4302.db.

To install BioConductor packages (if they are not already installed):

source("http://bioconductor.org/biocLite.R")
biocLite(c("annaffy", "mouse4302.db"))

Load the packages and extract annotation of the filtered ids:

library(annaffy)
annotation_table = aafTableAnn(filterd_ids, "mouse4302.db")
saveHTML(annotation_table, file="filtered.html")
browseURL("filtered.html")

## Loading required package: Biobase

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:dplyr':
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##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
##     union, unique, unsplit, which, which.max, which.min

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## Loading required package: GO.db

## Loading required package: AnnotationDbi

## Loading required package: stats4

## Loading required package: IRanges

## Loading required package: S4Vectors

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##     select

## 

## Loading required package: KEGG.db

## 
## KEGG.db contains mappings based on older data because the original
##   resource was removed from the the public domain before the most
##   recent update was produced. This package should now be considered
##   deprecated and future versions of Bioconductor may not have it
##   available.  Users who want more current data are encouraged to look
##   at the KEGGREST or reactome.db packages

## Loading required package: mouse4302.db

## Loading required package: org.Mm.eg.db

## 

## 

## Warning in chkPkgs(chip): The mouse4302.db package does not appear to contain
## annotation data.

## Warning in result_fetch(res@ptr, n = n): SQL statements must be issued with
## dbExecute() or dbSendStatement() instead of dbGetQuery() or dbSendQuery().

## Warning in result_fetch(res@ptr, n = n): SQL statements must be issued with
## dbExecute() or dbSendStatement() instead of dbGetQuery() or dbSendQuery().

## An object of class "aafTable"
## Slot "probeids":
## [1] "1416200_at"
## 
## Slot "table":
## $Probe
## An object of class "aafList"
## [[1]]
## An object of class "aafProbe"
## [1] "1416200_at"
## 
## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "Il33"
## attr(,"class")
## [1] "aafSymbol"
## 
## 
## $Description
## An object of class "aafList"
## [[1]]
## [1] "interleukin 33"
## attr(,"class")
## [1] "aafDescription"
## 
## 
## $Chromosome
## An object of class "aafList"
## [[1]]
## [1] "19"
## attr(,"class")
## [1] "aafChromosome"
## 
## 
## $`Chromosome Location`
## An object of class "aafList"
## [[1]]
## An object of class "aafChromLoc"
## [1] 29925112 29945789
## 
## 
## $GenBank
## An object of class "aafList"
## [[1]]
## [1] "NM_133775"
## attr(,"class")
## [1] "aafGenBank"
## 
## 
## $Gene
## An object of class "aafList"
## [[1]]
## An object of class "aafLocusLink"
## [1] 77125
## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.182359"
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## [1] "aafUniGene"
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## [[1]][[12]]
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## 
## [[1]][[17]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[18]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[19]]
## An object of class "aafGOItem"
## @id   "GO:0032689"
## @name "negative regulation of interferon-gamma production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[20]]
## An object of class "aafGOItem"
## @id   "GO:0032736"
## @name "positive regulation of interleukin-13 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[21]]
## An object of class "aafGOItem"
## @id   "GO:0032753"
## @name "positive regulation of interleukin-4 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[22]]
## An object of class "aafGOItem"
## @id   "GO:0032754"
## @name "positive regulation of interleukin-5 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[23]]
## An object of class "aafGOItem"
## @id   "GO:0032755"
## @name "positive regulation of interleukin-6 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[24]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[25]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[26]]
## An object of class "aafGOItem"
## @id   "GO:0045944"
## @name "positive regulation of transcription by RNA polymerase II"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[27]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
## @evid "IBA"
## 
## [[1]][[28]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[29]]
## An object of class "aafGOItem"
## @id   "GO:0051024"
## @name "positive regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[30]]
## An object of class "aafGOItem"
## @id   "GO:0051025"
## @name "negative regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[31]]
## An object of class "aafGOItem"
## @id   "GO:0051607"
## @name "defense response to virus"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[32]]
## An object of class "aafGOItem"
## @id   "GO:0061518"
## @name "microglial cell proliferation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[33]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[34]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[35]]
## An object of class "aafGOItem"
## @id   "GO:0097191"
## @name "extrinsic apoptotic signaling pathway"
## @type "Biological Process"
## @evid "IGI"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id     "04623"
## @name   "Cytosolic DNA-sensing pathway"
## @enzyme ""

Annotation

Generate separate annotations for the up-regulated and down-regulated differentially expressed genes between WT and KO. Discuss each set in a molecular context as detailed as possible.

Generating two lists of IDs for the up- and down- regulated genes

up_ids = ids[filter_up] # IDs of up-regulated genes
length(up_ids)

## [1] 95

head(up_ids)

## [1] "1416200_at"   "1416236_a_at" "1418203_at"   "1419394_s_at" "1419427_at"  
## [6] "1419591_at"

down_ids = ids[filter_down] # IDs of down-regulared genes
length(down_ids)

## [1] 181

head(down_ids)

## [1] "1417808_at" "1417932_at" "1418050_at" "1418100_at" "1418213_at"
## [6] "1418266_at"

Obtaining the annotation of the up- and down- regulated genes into tables

up_table = aafTableAnn(up_ids, "mouse4302.db")
head(up_table)
saveHTML(up_table, file="up_table.html")
browseURL("up_table.html")

down_table = aafTableAnn(down_ids, "mouse4302.db")
head(down_table)
saveHTML(down_table, file="down_table.html")
browseURL("down_table.html")

## An object of class "aafTable"
## Slot "probeids":
## [1] "1416200_at"
## 
## Slot "table":
## $Probe
## An object of class "aafList"
## [[1]]
## An object of class "aafProbe"
## [1] "1416200_at"
## 
## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "Il33"
## attr(,"class")
## [1] "aafSymbol"
## 
## 
## $Description
## An object of class "aafList"
## [[1]]
## [1] "interleukin 33"
## attr(,"class")
## [1] "aafDescription"
## 
## 
## $Chromosome
## An object of class "aafList"
## [[1]]
## [1] "19"
## attr(,"class")
## [1] "aafChromosome"
## 
## 
## $`Chromosome Location`
## An object of class "aafList"
## [[1]]
## An object of class "aafChromLoc"
## [1] 29925112 29945789
## 
## 
## $GenBank
## An object of class "aafList"
## [[1]]
## [1] "NM_133775"
## attr(,"class")
## [1] "aafGenBank"
## 
## 
## $Gene
## An object of class "aafList"
## [[1]]
## An object of class "aafLocusLink"
## [1] 77125
## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.182359"
## attr(,"class")
## [1] "aafUniGene"
## 
## 
## $PubMed
## An object of class "aafList"
## [[1]]
## An object of class "aafPubMed"
##   [1]  8889548 10349636 11042159 11076861 11217851 12466851 12477932 12819012
##   [9] 15475267 15489334 16141072 16141073 16286016 16602821 17185418 17492053
##  [17] 17623648 17675517 17881510 18003919 18023358 18250453 18268038 18450470
##  [25] 18552204 18566365 18603409 18667700 18799693 18802081 19248109 19451398
##  [33] 19465481 19508382 19553541 19559631 19661270 19666510 19684081 19750479
##  [41] 19841166 19892870 19933859 19950183 20035719 20042577 20385815 20412815
##  [49] 20427273 20501612 20534524 20634488 20689814 20693421 20937871 20939024
##  [57] 21190867 21220696 21239713 21239718 21267068 21268000 21281751 21308681
##  [65] 21349253 21357533 21422152 21454686 21469105 21494550 21515798 21557213
##  [73] 21642589 21646790 21677750 21703183 21703403 21734074 21797940 21835205
##  [81] 21873635 21887788 21945667 21949025 21949094 21972019 22013230 22013485
##  [89] 22119406 22142849 22180658 22198948 22258632 22267218 22270365 22294690
##  [97] 22307629 22323740 22329990 22331917 22370606 22371395 22460070 22542450
## [105] 22585447 22634619 22660580 22661085 22665806 22686327 22689946 22702477
## [113] 22782692 22802353 22922818 23006545 23071771 23093619 23132931 23148283
## [121] 23162017 23169007 23248269 23300625 23323935 23324173 23347081 23363980
## [129] 23397250 23403558 23418608 23496815 23499895 23523996 23547117 23582173
## [137] 23585480 23630360 23662055 23683462 23733876 23810766 23837438 23892028
## [145] 23894196 23911389 23918359 23945235 23954132 23960191 24028396 24043894
## [153] 24045639 24058536 24076135 24076431 24105680 24194600 24205109 24220317
## [161] 24257755 24356538 24446518 24459820 24551140 24556514 24586149 24613091
## [169] 24619410 24675360 24730559 24786898 24860117 24860190 24892809 24982172
## [177] 24985397 25015831 25022964 25043027 25143444 25153903 25172162 25278425
## [185] 25313073 25429071 25458701 25472995 25500143 25504587 25505285 25533952
## [193] 25543045 25573803 25599561 25617223 25617473 25660244 25661185 25676669
## [201] 25683166 25692703 25693767 25714839 25714983 25739051 25746970 25786179
## [209] 25795135 25807992 25808546 25814531 25829541 25847973 25857925 25870243
## [217] 25918379 25926677 25930197 25941360 25944738 25997709 26011644 26018806
## [225] 26044350 26047701 26079807 26092469 26100084 26129648 26200013 26230091
## [233] 26243875 26244295 26249267 26251474 26272855 26277897 26310268 26322482
## [241] 26342029 26352378 26363151 26365875 26386119 26417439 26425820 26428949
## [249] 26473724 26489077 26490658 26514775 26518437 26566691 26597162 26598236
## [257] 26602156 26603638 26673140 26755704 26771472 26802241 26811463 26813347
## [265] 26830114 26864308 26872602 26872699 26908008 26943125 26987428 26991049
## [273] 27001956 27053610 27056266 27067050 27091974 27102638 27120577 27126934
## [281] 27155324 27184849 27222019 27222477 27233150 27236500 27300306 27310495
## [289] 27317338 27318792 27340126 27358001 27372570 27421703 27421705 27453471
## [297] 27492144 27545879 27548066 27559047 27608599 27626380 27653694 27683753
## [305] 27697499 27701734 27749840 27775548 27811142 27821781 27839630 27918564
## [313] 27994070 28011865 28095415 28096407 28137851 28196763 28196875 28198366
## [321] 28228256 28258042 28259547 28341741 28359899 28373582 28389473 28401938
## [329] 28423665 28439013 28448566 28448579 28450225 28494352 28576981 28607531
## [337] 28637902 28637954 28656964 28675392 28697404 28701507 28710436 28730605
## [345] 28771101 28802996 28889151 28921511 29032512 29045819 29045902 29045903
## [353] 29146574 29147584 29166590 29208683 29222107 29242561 29247993 29288203
## [361] 29309756 29368135 29379874 29392836 29420261 29452935 29463593 29467297
## [369] 29534857 29610934 29621782 29671873 29674622 29728120 29728508 29729112
## [377] 29906552 29950152 29954866 30001716 30098206 30224451 30539029 30575775
## [385] 30602786 30642984 30763587
## 
## 
## $`Gene Ontology`
## An object of class "aafList"
## [[1]]
## An object of class "aafGO"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id   "GO:0002281"
## @name "macrophage activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[2]]
## An object of class "aafGOItem"
## @id   "GO:0002282"
## @name "microglial cell activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[3]]
## An object of class "aafGOItem"
## @id   "GO:0002686"
## @name "negative regulation of leukocyte migration"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[4]]
## An object of class "aafGOItem"
## @id   "GO:0002826"
## @name "negative regulation of T-helper 1 type immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[5]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[6]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[7]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IBA"
## 
## [[1]][[8]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IDA"
## 
## [[1]][[9]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IPI"
## 
## [[1]][[10]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "ISO"
## 
## [[1]][[11]]
## An object of class "aafGOItem"
## @id   "GO:0005576"
## @name "extracellular region"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[12]]
## An object of class "aafGOItem"
## @id   "GO:0005615"
## @name "extracellular space"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[13]]
## An object of class "aafGOItem"
## @id   "GO:0005634"
## @name "nucleus"
## @type "Cellular Component"
## @evid "ISO"
## 
## [[1]][[14]]
## An object of class "aafGOItem"
## @id   "GO:0005694"
## @name "chromosome"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[15]]
## An object of class "aafGOItem"
## @id   "GO:0010628"
## @name "positive regulation of gene expression"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[16]]
## An object of class "aafGOItem"
## @id   "GO:0031410"
## @name "cytoplasmic vesicle"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[17]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[18]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[19]]
## An object of class "aafGOItem"
## @id   "GO:0032689"
## @name "negative regulation of interferon-gamma production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[20]]
## An object of class "aafGOItem"
## @id   "GO:0032736"
## @name "positive regulation of interleukin-13 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[21]]
## An object of class "aafGOItem"
## @id   "GO:0032753"
## @name "positive regulation of interleukin-4 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[22]]
## An object of class "aafGOItem"
## @id   "GO:0032754"
## @name "positive regulation of interleukin-5 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[23]]
## An object of class "aafGOItem"
## @id   "GO:0032755"
## @name "positive regulation of interleukin-6 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[24]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[25]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[26]]
## An object of class "aafGOItem"
## @id   "GO:0045944"
## @name "positive regulation of transcription by RNA polymerase II"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[27]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
## @evid "IBA"
## 
## [[1]][[28]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[29]]
## An object of class "aafGOItem"
## @id   "GO:0051024"
## @name "positive regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[30]]
## An object of class "aafGOItem"
## @id   "GO:0051025"
## @name "negative regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[31]]
## An object of class "aafGOItem"
## @id   "GO:0051607"
## @name "defense response to virus"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[32]]
## An object of class "aafGOItem"
## @id   "GO:0061518"
## @name "microglial cell proliferation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[33]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[34]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[35]]
## An object of class "aafGOItem"
## @id   "GO:0097191"
## @name "extrinsic apoptotic signaling pathway"
## @type "Biological Process"
## @evid "IGI"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id     "04623"
## @name   "Cytosolic DNA-sensing pathway"
## @enzyme ""

## An object of class "aafTable"
## Slot "probeids":
## [1] "1417808_at"
## 
## Slot "table":
## $Probe
## An object of class "aafList"
## [[1]]
## An object of class "aafProbe"
## [1] "1417808_at"
## 
## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "2310050C09Rik"
## attr(,"class")
## [1] "aafSymbol"
## 
## 
## $Description
## An object of class "aafList"
## [[1]]
## [1] "RIKEN cDNA 2310050C09 gene"
## attr(,"class")
## [1] "aafDescription"
## 
## 
## $Chromosome
## An object of class "aafList"
## [[1]]
## [1] "3"
## attr(,"class")
## [1] "aafChromosome"
## 
## 
## $`Chromosome Location`
## An object of class "aafList"
## [[1]]
## An object of class "aafChromLoc"
## [1] -92868358
## 
## 
## $GenBank
## An object of class "aafList"
## [[1]]
## [1] "NM_025621"
## attr(,"class")
## [1] "aafGenBank"
## 
## 
## $Gene
## An object of class "aafList"
## [[1]]
## An object of class "aafLocusLink"
## [1] 66533
## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.144259"
## attr(,"class")
## [1] "aafUniGene"
## 
## 
## $PubMed
## An object of class "aafList"
## [[1]]
## An object of class "aafPubMed"
## [1] 10349636 11042159 11076861 11217851 12466851 16141072 16141073 21267068
## [9] 21873635
## 
## 
## $`Gene Ontology`
## An object of class "aafList"
## [[1]]
## An object of class "aafGO"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id   "GO:0001533"
## @name "cornified envelope"
## @type "Cellular Component"
## @evid "IBA"
## 
## [[1]][[2]]
## An object of class "aafGOItem"
## @id   "GO:0005198"
## @name "structural molecule activity"
## @type "Molecular Function"
## @evid "IBA"
## 
## [[1]][[3]]
## An object of class "aafGOItem"
## @id   "GO:0005737"
## @name "cytoplasm"
## @type "Cellular Component"
## @evid "IBA"
## 
## [[1]][[4]]
## An object of class "aafGOItem"
## @id   "GO:0018149"
## @name "peptide cross-linking"
## @type "Biological Process"
## @evid "IBA"
## 
## [[1]][[5]]
## An object of class "aafGOItem"
## @id   "GO:0030216"
## @name "keratinocyte differentiation"
## @type "Biological Process"
## @evid "IBA"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## list()

Down Regulation of Irf6

We condcuted 10^{6} statistical tests. The computed p-values need to be corrected for multiple testing. The correction can be performed using p.adjust, which simply takes the orignial p-values a vector and returns the adjusted (corrected) p-values:

adjusted.pvalues = p.adjust(pvalue, method = "fdr")

Number original p-values \(\leq\) 0.05 = 5099 while the number adjusted (corrected) p-values < 0.05 \(\geq\) 9

Here is an example of the original p-values and corresponding adjusted p-values:

• Identify the top 10 biologically significant genes (i.e., by fold-change)

• Identify the top 10 statistically significant genes (i.e., by p-value)

• Identify the overlapping (if any) between the two sets of genes